Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification

Area of Science:

• Lipidomics
• Mass Spectrometry
• Computational Biology

Background:

• Accurate lipid identification is crucial for understanding cellular functions and diseases.
• Existing software for lipid identification from MS/MS spectra can be costly or lack advanced features compared to proteomics tools.

Purpose of the Study:

• To develop Greazy, an open-source software tool for automated phospholipid identification from MS/MS spectra.
• To adapt sophisticated proteomics methods for lipid identification.
• To evaluate the performance and utility of Greazy/LipidLama.

Main Methods:

• Greazy builds a phospholipid search space and theoretical MS/MS spectra based on user parameters.
• It scores experimental spectra using hypergeometric distribution and intensity-based metrics.
• LipidLama filters results using mixture modeling and density estimation.

Main Results:

• Greazy demonstrated high accuracy in identifying multiple lipid classes when compared against the NIST 2014 metabolomics library.
• Comparison with commercial software LipidSearch showed considerable differences in identified spectra but good agreement on shared identifications.
• The tool proved effective with data from various instruments, including Orbitrap and Q-TOF.

Conclusions:

• Greazy/LipidLama successfully applies proteomics-derived methods to lipid identification.
• The open-source nature and demonstrated utility make it a valuable tool for the lipidomics community.
• This advancement facilitates more comprehensive lipid analysis in biological and disease research.